Academic literature on the topic 'Smooth texture'
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Journal articles on the topic "Smooth texture"
Lluscà, Marta, Aldrin Antony, and Joan Bertomeu. "Growth and Properties of ZnO:Al on Textured Glass for Thin Film Solar Cells." International Journal of Photoenergy 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/406495.
Full textCadoret, G., and A. M. Smith. "Friction, not texture, dictates grip forces used during object manipulation." Journal of Neurophysiology 75, no. 5 (May 1, 1996): 1963–69. http://dx.doi.org/10.1152/jn.1996.75.5.1963.
Full textArdani, Ahmad. "Portland Cement Concrete Pavement Texturing Methods." Transportation Research Record: Journal of the Transportation Research Board 1544, no. 1 (January 1996): 17–23. http://dx.doi.org/10.1177/0361198196154400103.
Full textMerriman, T., and J. Kannel. "Analyses of the Role of Surface Roughness on Contact Stresses Between Elastic Cylinders With and Without Soft Surface Coating." Journal of Tribology 111, no. 1 (January 1, 1989): 87–94. http://dx.doi.org/10.1115/1.3261883.
Full textZhang, Zixin, Zhuangzhuang Chu, and Xuli Zhu. "Frictional effect of spherical convex textured rigid bodies sliding on smooth PDMS." E3S Web of Conferences 145 (2020): 02055. http://dx.doi.org/10.1051/e3sconf/202014502055.
Full textCarney, Elizabeth, Kameron Moding, Susan Johnson, and Jennifer Fisher. "Effects of Texture on Vegetable Acceptance During Infancy and Toddlerhood." Current Developments in Nutrition 4, Supplement_2 (May 29, 2020): 952. http://dx.doi.org/10.1093/cdn/nzaa054_024.
Full textWang, Guodong, Zhenkuan Pan, Qian Dong, Ximei Zhao, Zhimei Zhang, and Jinming Duan. "Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information." Journal of Applied Mathematics 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/614613.
Full textZhang, Na, Fazhan Yang, Fulin Jiang, and Guohua Liu. "Study of the effect of surface laser texture on tribological properties of cemented carbide materials." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 234, no. 6-7 (January 30, 2020): 993–1006. http://dx.doi.org/10.1177/0954405419896116.
Full textTong, Xin, Shucai Yang, Xianli Liu, Weiwei Liu, and Chunsheng He. "Friction, wear, and fatigue analysis for micro-textured cemented carbide." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 17 (July 10, 2019): 5989–6004. http://dx.doi.org/10.1177/0954406219862588.
Full textDu, Hongwei, Yunfeng Zhang, Fangxun Bao, Ping Wang, and Caiming Zhang. "A Texture Preserving Image Interpolation Algorithm Based on Rational Function." International Journal of Multimedia Data Engineering and Management 9, no. 2 (April 2018): 36–56. http://dx.doi.org/10.4018/ijmdem.2018040103.
Full textDissertations / Theses on the topic "Smooth texture"
Ben, Ali Imed Eddine. "Wetting, Adhesion and friction investigations of hetero-chemical smooth patterned surfaces." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1250/document.
Full textMicro and Nanoscale surface patterns are considered as potential templates and building blocks for Micro/nanotechnology. As for materials in general, these micro /nano-scale surface structures have been of increasing research interest in recent years, due to their unique properties. They are expected to exhibit novel and significantly improved physical, chemical, mechanical and other properties, as well as to offer opportunities for manifestation of new phenomena and processes. In the present PhD work, we propose a multiple scale analysis of the adhesion, friction and wetting behaviors for different patterned interfaces. In a first chapter, we developed a general methodology to design well-defined surfaces combining micro-contact printing (µCP), self-assembled monolayers (SAMs) and polymer grafting techniques. Then we study the wettability of a patterned solid surface. Where, the stick-slip regime, and the effect of the patterning at the mesoscale was investigated. Furthermore, we concentrate on the dependence of adhesion and friction between a polymer and a rigid tip on the composition of the patterned substrates using a JKR, FFM and friction machines. Intriguingly, the uses of these approaches did not provide us with a clear answer to our bewilderment. Therefore, in the third chapter, we adopted the approach of the dewetting of thin polymer film on top of patterned surfaces. We study the impact of the solid/liquid boundary condition on the evolution of the rim instability during the course of dewetting. The last chapter details the investigation of the predominant aspect between the chemistry introduced on the surface and the mechanical proprieties of the substrate
Chang, Jason Ph D. Massachusetts Institute of Technology. "Extracting orientation and scale from smoothly varying textures with application to segmentation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55147.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 109-112).
The work in this thesis focuses on two main computer vision research topic: image segmentation and texture modeling. Information theoretic measures have been applied to image segmentation algorithms for the past decade. In previous work, common measures such as mutual information or J divergence have been used. Algorithms typically differ by the measure they use and the features they use to segment an image. When both the information measure and the features change, it is difficult to compare which algorithm actually performs better and for what reason. Though we do not provide a solution to this problem, we do compare and contrast three distances under two different measures. This thesis considers two forms of information theoretic based image segmentation algorithms that have previously been considered. We denote them here as the label method and the conditional method. Gradient ascent velocities are derived for a general Ali-Silvey distance for both methods, and a unique bijective mapping is shown to exist between the two methods when the Ali-Silvey distance takes on a specific form. While the conditional method is more commonly considered, it is implicitly limited by a two-region segmentation by construction. Using the derived mapping, one can easily extend a binary segmentation algorithm based on the conditional method to a multiregion segmentation algorithm based on the label method. The importance of initializations and local extrema is also considered, and a method of multiple random initializations is shown to produce better results.
(cont.) Additionally, segmentation results and methods for comparing the utility of the different measures are presented. This thesis also considers a novel texture model for representing textured regions with smooth variations in orientation and scale. By utilizing the steerable pyramid of Simoncelli and Freeman, the textured regions of natural images are decomposed into explicit local attributes of contrast, bias, scale, and orientation. Once found, smoothness in these attributes are imposed via estimation of Markov random fields. This combination allows for demonstrable improvements in common scene analysis applications including segmentation, reflectance and shading estimation, and estimation of the radiometric response function from a single grayscale image.
by Jason Chang.
S.M.
Gallardo, Mathias. "Contributions to Monocular Deformable 3D Reconstruction : Curvilinear Objects and Multiple Visual Cues." Thesis, Université Clermont Auvergne (2017-2020), 2018. http://www.theses.fr/2018CLFAC021/document.
Full textMonocular deformable 3D reconstruction is the general problem of recovering the 3D shape of a deformable object from monocular 2D images. Several scenarios have emerged: the Shape-from-Template (SfT) and the Non-Rigid Structure-from-Motion (NRSfM) are two approaches intensively studied for their practicability. The former uses a single image depicting the deforming object and a template (a textured 3D shape of this object in a reference pose). The latter does not use a template, but uses several images and recovers the 3D shape in each image. Both approaches rely on the motion of correspondences between the images and deformation priors, which restrict their use to well-textured surfaces which deform smoothly. This thesis advances the state-of-the-art in SfT and NRSfM in two main directions. The first direction is to study SfT for the case of 1D templates (i.e. curved, thin structures such as ropes and cables). The second direction is to develop algorithms in SfT and NRSfM that exploit multiple visual cues and can solve complex, real-world cases which were previously unsolved. We focus on isometric deformations and reconstruct the outer part of the object. The technical and scientific contributions of this thesis are divided into four parts. The first part of this thesis studies the case of a curvilinear template embedded in 2D or 3D space, referred to Curve SfT. We propose a thorough theoretical analysis and practical solutions for Curve SfT. Despite its apparent simplicity, Curve SfT appears to be a complex problem: it cannot be solved locally using exact non-holonomic partial differential equation and is only solvable up to a finite number of ambiguous solutions. A major technical contribution is a computational solution based on our theory, which generates all the ambiguous solutions.The second part of this thesis deals with a limitation of SfT methods: reconstructing creases. This is due to the sparsity of the motion constraint and regularization. We propose two contributions which rely on a non-convex energy minimization framework. First, we complement the motion constraint with a robust boundary contour constraint. Second, we implicitly model creases with a dense mesh-based surface representation and an associated robust smoothing constraint, which deactivates curvature smoothing automatically where needed, without knowing a priori the crease location. The third part of this thesis is dedicated to another limitation of SfT: reconstructing poorly-textured surfaces. This is due to correspondences which cannot be obtained so easily on poorly-textured surfaces (either sparse or dense). As shading reveals details on poorly-textured surfaces, we propose to combine shading and SfT. We have two contributions. The first is a cascaded initialization which estimates sequentially the surface's deformation, the scene illumination, the camera response and then the surface albedos from deformed monocular images. The second is to integrate shading to our previous energy minimization framework for simultaneously refining deformation and photometric parameters.The last part of this thesis relaxes the knowledge of the template and addresses two limitations of NRSfM: reconstructing poorly-textured surfaces with creases. Our major contribution is an extension of the second framework to recover jointly the 3D shapes of all input images and the surface albedos without any template
Vaikuntanathan, Visakh. "Experimental and Theoretical Studies of Liquid Drop Impact on Solid Surfaces Comprising Smooth and Texture Portions." Thesis, 2015. http://etd.iisc.ernet.in/2005/3939.
Full text(8333136), Mohammed D. Aldosari. "Mobile LiDAR for Monitoring MSE Walls with Smooth and Textured Precast Concrete Panels." Thesis, 2020.
Find full textBooks on the topic "Smooth texture"
Bentley, Joyce. Smooth. North Mankato, MN: Chrysalis Education, 2005.
Find full textMichele, Tracey. Rough and smooth. North Mankato, Minn: Smart Apple Media, 2006.
Find full textSmooth or rough. Chicago, Ill: Heinemann Library, 2008.
Find full textDawson, Emily C. Rough and smooth. Mankato, Minnesota: Amicus, 2012.
Find full textDawson, Emily C. Rough and smooth. Mankato, Minnesota: Amicus, 2012.
Find full textRough or smooth. Chicago: Raintree, 2005.
Find full textBrocket, Jane. Spiky, Slimy, Smooth: What Is Texture? Lerner Publishing Group, 2011.
Find full textBrocket, Jane. Spiky, Slimy, Smooth: What Is Texture? Lerner Publishing Group, 2010.
Find full textSpiky Slimy Smooth What Is Texture. Millbrook Press, 2011.
Find full textLittle Nippers: Is It - Rough or Smooth (Little Nippers). Heinemann Library, 2004.
Find full textBook chapters on the topic "Smooth texture"
Gårding, Jonas. "Shape from texture for smooth curved surfaces." In Computer Vision — ECCV'92, 630–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55426-2_67.
Full textShaila, S. G., and A. Vadivel. "Smooth Weighted Colour Histogram Using Human Visual Perception for Content-Based Image Retrieval Applications." In Textual and Visual Information Retrieval using Query Refinement and Pattern Analysis, 77–92. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2559-5_4.
Full text"smooth texture." In Dictionary Geotechnical Engineering/Wörterbuch GeoTechnik, 1257. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41714-6_194784.
Full text"Texture image segmentation with smooth gradients and local information." In Computational Modelling of Objects Represented in Images III, 137–42. CRC Press, 2012. http://dx.doi.org/10.1201/b12753-28.
Full textProbert Smith, Penelope. "Smooth and Rough Target Modelling: Examples in Mapping and Texture Classification." In World Scientific Series in Robotics and Intelligent Systems, 61–78. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812811141_0005.
Full textKeefer, Robert F. "Physical Properties of Soils." In Handbook of Soils for Landscape Architects. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195121025.003.0006.
Full textPurves, Alex. "Rough Reading." In Experience, Narrative, and Criticism in Ancient Greece, 172–87. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198848295.003.0009.
Full textBurden, Mark. "Editing Shadows." In Textual Transformations, 173–91. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198808817.003.0010.
Full text"Summary and Discussion." In Advances in Linguistics and Communication Studies, 134–49. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6344-0.ch006.
Full textGraumann, Thomas. "The Structure and Elements of the ‘Ideal’ Session-Record and the Role of ‘Editing’." In The Acts of the Early Church Councils, 257–62. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198868170.003.0016.
Full textConference papers on the topic "Smooth texture"
Nguyen, Thanh Tuan, Thanh Phuong Nguyen, and Frederic Bouchara. "Smooth-Invariant Gaussian Features for Dynamic Texture Recognition." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803449.
Full textChung, Yu-Min, Michael Hull, and Austin Lawson. "Smooth Summaries of Persistence Diagrams and Texture Classification." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00428.
Full textWatson, Cori, Wisher Paudel, Houston G. Wood, and Brian K. Weaver. "Quantifying the Linearity of the Fluid Dynamics for Noncontacting Annular Seals." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-66804.
Full textStarck, Jean-Luc, Mikael Elad, and David L. Donoho. "Image decomposition: separation of texture from piecewise smooth content." In Optical Science and Technology, SPIE's 48th Annual Meeting, edited by Michael A. Unser, Akram Aldroubi, and Andrew F. Laine. SPIE, 2003. http://dx.doi.org/10.1117/12.507447.
Full textLarsen-Basse, Jorn, Lewis Ives, and Stephen M. Hsu. "Boundary Lubricated Friction Experiments With Coarse Surface Texture." In World Tribology Congress III. ASMEDC, 2005. http://dx.doi.org/10.1115/wtc2005-63355.
Full textCheng, Jiangtao, Aref Vandadi, and Chung-Lung Chen. "Condensation Heat Transfer on Two-Tier Superhydrophobic Surfaces." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-85841.
Full textPapadopoulos, C. I., P. G. Nikolakopoulos, and L. Kaiktsis. "Characterization of Stiffness and Damping in Textured Sector-Pad Micro- Thrust Bearings Using Computational Fluid Dynamics." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-69403.
Full textPothen, Zania S., and Stephen Nuske. "Texture-based fruit detection via images using the smooth patterns on the fruit." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487722.
Full textLi, Wei, Si-pu Guo, Xiao-peng Zhou, David J. Kukulka, and Jin-liang Xu. "A General Correlation for Condensation Heat Transfer in Micro-Fin for Herringbone and Dimple-Texture Tubes." In ASME 2015 13th International Conference on Nanochannels, Microchannels, and Minichannels collocated with the ASME 2015 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/icnmm2015-48198.
Full textLiu, Zhenyu, and Yi Wan. "A robust denoising method for random-valued impulse noise based on smooth and texture region separation." In 2012 International Conference on Systems and Informatics (ICSAI). IEEE, 2012. http://dx.doi.org/10.1109/icsai.2012.6223406.
Full textReports on the topic "Smooth texture"
Martin, S. J., K. O. Wessendorf, C. T. Gebert, G. C. Frye, R. W. Cernosek, L. Casaus, and M. A. Mitchell. Measuring liquid properties with smooth- and textured-surface resonators. Office of Scientific and Technical Information (OSTI), August 1993. http://dx.doi.org/10.2172/139545.
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